batch_size = n# 每次读取一小部分数据作为当前的训练数据来执行反向传播算法x = tf.placeholder(tf.float32, shape=(batch_size, feature_num), name="x-input")y_= tf.placeholder(tf.float32, shape=(batch_size, 1), name='y-input')# 定义神经网络结构和优化算法loss = ...# loss = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))    ## cross_entropy(交叉熵)learning_rate = 0.001#定义反向传播算法 train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)  # 优化算法# 训练神经网络with tf.Session() as sess:    # 参数初始化    init_op = tf.global_variables_initializer()    sess.run(init_op)        ...        # 训练模型。    STEPS = ...    # 迭代的更新参数    for i in range(STEPS):        # 准备batch_size个训练数据。一般将所有训练数据打乱之后再选取可以得到更好的优化效果。        current_X, current_Y = ...        # 对`tf.GraphKeys.TRAINABLE_VARIABLES`集合中的变量进行优化，使得当前batch下损失最小        sess.run(train_step, feed_dict = {x : current_X, y : current_Y})